// Copyright (C) 2009 International Business Machines.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// Author:  Andreas Waechter               IBM    2009-04-02

// This file is part of the Ipopt tutorial.  It is the skeleton for
// the C++ implementation of the coding exercise problem (in AMPL
// formulation):
//
// param n := 4;
//
// var x {1..n} <= 0, >= -1.5, := -0.5;
//
// minimize obj:
//   sum{i in 1..n} (x[i]-1)^2;
//
// subject to constr {i in 2..n-1}:
//   (x[i]^2+1.5*x[i]-i/n)*cos(x[i+1]) - x[i-1] = 0;
//
// The constant term "i/n" in the constraint is supposed to be input data

#include "TutorialCpp_nlp.hpp"

#include <cassert>
#include <cstdio>

// We use sin and cos
#include <cmath>

using namespace Ipopt;

// constructor
TutorialCpp_NLP::TutorialCpp_NLP(
   Index         N,
   const Number* a
)
   : N_(N)
{
   // Copy the values for the constants appearing in the constraints
   a_ = new Number[N_ - 2];
   for( Index i = 0; i < N_ - 2; i++ )
   {
      a_[i] = a[i];
   }
}

//destructor
TutorialCpp_NLP::~TutorialCpp_NLP()
{
   // make sure we delete everything we allocated
   delete[] a_;
}

// returns the size of the problem
bool TutorialCpp_NLP::get_nlp_info(
   Index&          n,
   Index&          m,
   Index&          nnz_jac_g,
   Index&          nnz_h_lag,
   IndexStyleEnum& index_style
)
{
   // number of variables is given in constructor
   n =;

   // we have N_-2 constraints
   m =;

   // each constraint has three nonzeros
   nnz_jac_g =;

   // We have the full diagonal, and the first off-diagonal except for
   // the first and last variable
   nnz_h_lag =;

   // use the C style indexing (0-based) for the matrices
   index_style = TNLP::C_STYLE;

   return true;
}

// returns the variable bounds
bool TutorialCpp_NLP::get_bounds_info(
   Index   n,
   Number* x_l,
   Number* x_u,
   Index   m,
   Number* g_l,
   Number* g_u
)
{
   // here, the n and m we gave IPOPT in get_nlp_info are passed back to us.
   // If desired, we could assert to make sure they are what we think they are.
   //assert(n == );
   //assert(m == );

   // HERE SET THE BOUNDS ON VARIABLE

   return true;
}

// returns the initial point for the problem
bool TutorialCpp_NLP::get_starting_point(
   Index   n,
   bool    init_x,
   Number* x,
   bool    init_z,
   Number* z_L,
   Number* z_U,
   Index   m,
   bool    init_lambda,
   Number* lambda
)
{
   // Here, we assume we only have starting values for x, if you code
   // your own NLP, you can provide starting values for the dual variables
   // if you wish
   assert(init_x == true);
   assert(init_z == false);
   assert(init_lambda == false);

   // initialize to the given starting point

#ifdef skip_me
   /* If checking derivatives, if is useful to choose different values */
#endif

   return true;
}

// returns the value of the objective function
// sum{i in 1..n} (x[i]-1)^2;
bool TutorialCpp_NLP::eval_f(
   Index         n,
   const Number* x,
   bool          new_x,
   Number&       obj_value
)
{
   // HERE COMPUTE VALUE OF OBJECTIVE FUNCTION
   obj_value = ...

               return true;
}

// return the gradient of the objective function grad_{x} f(x)
bool TutorialCpp_NLP::eval_grad_f(
   Index         n,
   const Number* x,
   bool          new_x,
   Number*       grad_f
)
{
   // HERE SET ALL VALUES OF GRADIENT IN grad_f

   return true;
}

// return the value of the constraints: g(x)
// (x[j+1]^2+1.5*x[j+1]-a[j])*cos(x[j+2]) - x[j] = 0;
bool TutorialCpp_NLP::eval_g(
   Index         n,
   const Number* x,
   bool          new_x,
   Index         m,
   Number*       g
)
{
   // HERE COMPUTE VALUE OF ALL CONSTRAINTS IN g

   return true;
}

// return the structure or values of the jacobian
bool TutorialCpp_NLP::eval_jac_g(
   Index         n,
   const Number* x,
   bool          new_x,
   Index         m,
   Index         nele_jac,
   Index*        iRow,
   Index*        jCol,
   Number*       values
)
{
   if( values == NULL )
   {
      // return the structure of the jacobian

      // HERE FILL iRow and jCol
   }
   else
   {
      // return the values of the jacobian of the constraints

      // HERE FILL values
   }

   return true;
}

//return the structure or values of the hessian
bool TutorialCpp_NLP::eval_h(
   Index         n,
   const Number* x,
   bool          new_x,
   Number        obj_factor,
   Index         m,
   const Number* lambda,
   bool          new_lambda,
   Index         nele_hess,
   Index*        iRow,
   Index*        jCol,
   Number*       values
)
{
   if( values == NULL )
   {
      // return the structure (lower or upper triangular part) of the
      // Hessian of hte Lagrangian function

      // HERE FILL iRow and jCol

   }
   else
   {
      // return the values of the Hessian of hte Lagrangian function

      // HERE FILL values
   }

   return true;
}

void TutorialCpp_NLP::finalize_solution(
   SolverReturn               status,
   Index                      n,
   const Number*              x,
   const Number*              z_L,
   const Number*              z_U,
   Index                      m,
   const Number*              g,
   const Number*              lambda,
   Number                     obj_value,
   const IpoptData*           ip_data,
   IpoptCalculatedQuantities* ip_cq
)
{
   // here is where we would store the solution to variables, or write
   // to a file, etc so we could use the solution.

   printf("\nWriting solution file solution.txt\n");
   FILE* fp = fopen("solution.txt", "w");

   // For this example, we write the solution to the console
   fprintf(fp, "\n\nSolution of the primal variables, x\n");
   for( Index i = 0; i < n; i++ )
   {
      fprintf(fp, "x[%d] = %e\n", (int)i, x[i]);
   }

   fprintf(fp, "\n\nSolution of the bound multipliers, z_L and z_U\n");
   for( Index i = 0; i < n; i++ )
   {
      fprintf(fp, "z_L[%d] = %e\n", (int)i, z_L[i]);
   }
   for( Index i = 0; i < n; i++ )
   {
      fprintf(fp, "z_U[%d] = %e\n", (int)i, z_U[i]);
   }

   fprintf(fp, "\n\nObjective value\n");
   fprintf(fp, "f(x*) = %e\n", obj_value);
   fclose(fp);
}
